IMPACT OF RESTATEMENT OF EARNINGS ON TRADING METRICS
Duong Nguyen*, Shahid S. Hamid**, Suchi Mishra**, Arun Prakash**
Address for correspondence: Duong Nguyen, PhD
Assistant Professor of Finance, Department of Accounting and Finance Charlton College of Business
University of Massachusetts Dartmouth 285 Old Westport Road
North Dartmouth, MA 02747 Voice: 508-999-8070
* University of Massachusetts, Dartmouth, MA
** Florida International University, Miami, FL
Impact of Restatement of Earnings on Trading Metrics
This study uses a more recent sample of matched restating firms from 1997 to 2002 to investigate the microstructure impact on a broad spectrum of key trading metrics including return, volume, and particularly the spread for stocks of the restating firms. Daily spreads of firms in the sample are examined around the restatement announcement dates in order to test for evidence of increased adverse selection. We also investigate the effect of restatement announcements on measures of trading activity and on the relation of these measures to the bid-ask spread. We find that restatement produce substantial change in volume and spread after the announcement. Specifically, spread increases dramatically at day 0 and day 1 (relative to the announcement day), and persistently go back the normal level after that. Significant increases in trading volume begin at the announcement date and go back the normal level after about 10 trading days. This is consistent with the models of Kyle (1985), Easley and O’Hara (1982) and Kim and Verrecchia (1991a, 1991b) in that increased information asymmetry at announcement dates should result in higher trading volumes as well as increased spreads. We also find that announcements produce negative abnormal returns. We also postulate a cross sectional model in which spread is a function of normal trading volume, unusual trading volume, and return variability. As predicted by the inventory control model, we find that spread is negatively correlated to trading volume but positively correlated to return variability. I. Introduction
Restatement of financial statements and its consequences are becoming an important
issue among the investors, corporate management, regulators, and auditing firms, particularly in
the aftermath of the Sarbanes-Oxley Act of 2002. Investors and regulators are concerned over
restatements to correct non-GAAP accounting in previously issued financial statements. For
example, the former SEC Chairman testified before a Senate Subcommittee that, “in recent
years, countless investors have suffered significant losses as market capitalization have dropped
by billions of dollars due to restatements of audited financial statements” (Levitt, 2000). While
dramatic declines in market values do occur, the research on the impact of such restatements,
while increasing, is still rather limited. This paper studies how financial statement restatement
announcements affect the trading activity in the stock market using a sample of restatement
1
announcements from 1997 to 2002 collected from GAO-03395R Financial Statement
Restatement Database.1
There has been a recent upsurge in interest in issues concerning restatement. Palmrose,
Richardson and Scholz (2004) using a sample of 403 restatements from 1995 to 1999 showed
that stock market reaction depended on the characteristics of the restatement. Usually
announcements resulted in negative abnormal returns for income decreasing restatements,
dispersion increased, and there was no effect on bid-ask spread. Desai, Hogan, and Wilkins
(2006) investigate the impact on adverse managerial reputations and penalties imposed by both
the labor market and regulators. Srinivasan (2005) also showed that directors of companies that
have restatements incur significant labor market penalties. Akhigbe, Kudla, and Madura (2005)
also find negative market reaction particularly if restatement is due to corrections in revenue
estimates and when revised earning lead to revised expectations of future earnings. The impact
on litigation is investigated by Pensrose and Scholz (2004) and the tax consequences are
examined by Erickson, Hanlon, and Maydew (2004). The effect on expectations of future
earnings and on cost of capital are studied by Hribar and Jenkins (2004). Griffin (2003) studied
the response of analysts, insiders, short sellers and institutions to restatements.
This study uses a more recent sample from 1997 to 2002 to investigate the microstructure
impact on a broader spectrum of key trading metrics including return, volume, and particularly
the spread for stocks of the restating firms. Daily spreads of firms in the sample are examined
around the restatement announcement dates in order to test for evidence of increased adverse
selection. We also investigate the effect of restatement announcements on measures of trading
activity and on the relation of these measures to the bid-ask spread.
1 The database is created by the United States General Accounting Office, Washington, DC 20548.
2
There are two principal theories that explain the bid-ask spread: (1) asymmetric
information model and (2) inventory control model. In asymmetric model, dealers (market
makers) trade with liquidity traders and informed traders. The latter groups have information
which is superior to the dealers, so bid and ask prices are set in order to compensate dealers for
the perceived adverse selection risk. Kyle (1985), Easley and O’Hara (1987), Glosten and
Milgrom (1985) all argue that if marker conditions are such that dealers become concerned that
there is a higher proportion of informed traders in the market or that the informed traders have
better information, they will widen bid-ask spread to compensate for the adverse selection risk.
These studies suggest a positive relationship between spreads and unusually high trading
volume, since dealers interpret an unusually high volume as a sign of an increased number of
informed traders and widen their spreads accordingly.
These relationships should be particular evident around the announcement dates since
these time would present an opportunity for information to be asymmetrically distributed. The
prediction of the adverse selection models is that spread should widen before an announcement
as there is increased probability that trades are initiated by investors with superior information,
while spreads should fall after an announcement, once the information has become public.
However, it is possible that within context of these models, spreads may not fall immediately
after the announcement, as there is still some advantage to be gained by market participants who
did not have superior information but have superior-information processing abilities. For
example, Kim and Verrecchia (1994) argue that directors or corporate insiders may have superior
information but they are prohibited from trading before the announcement dates, so they are able
to make use of it only after the announcements. Therefore, Kim and Verrecchia (1994) suggest
that disclosure of information would cause increased information asymmetry risk, so that spread
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should widen after the announcement rather than before it. However, in either case, one would
expect spreads to return to normal levels within a few days of the announcement.
Furthermore, Kim and Verrecchia (1991a, 1991b) argue that heterogeneous beliefs
around the corporate announcements induce market participants to trade. Therefore, they suggest
that increased information asymmetry at announcement dates should result in higher trading
volumes as well as increased spreads.
According to inventory control model, risk-averse market makers have a desired
(optimal) inventory position. To maintain this optimal inventory level, the market makers are
facing two types of risk: (1) the risk of being unable to trade the stock and (2) the risk that prices
will change while stocks are being held. Amihud and Mendelson (1980) and Ho and Stoll (1980)
argue that the higher the first risk, the more difficult for the market makers to return to their
optimal inventory level. In a liquid market characterized by high trading volumes, the dealer
(market maker) will only set a narrow inventory spread, since he/she is assured of being able to
quickly restore an out-of-equilibrium position. The inventory model, therefore, predict that as the
liquidity of stock increases (i.e., trading volume increases), the market maker will reduce the
spread since the compensation during this period is lower, resulting in a negative relationship
between trading volumes and spreads.
The second feature of inventory risk is related to the underlying variability of the stock
return. Garber and Silber (1979) and Ho and Stoll (1981) show that the more volatile the stock
price is, the more the market maker is exposed to the risk of adverse price movements, and
consequently, the spread will be wider to compensate the market maker, leading to a positive
relation between return variability and the spread.
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Finally, besides adverse selection and inventory components as discussed above, Roll
(1984) and Stoll (1989) identify another component of bid-ask spread which is order processing
cost. According to the order processing cost model, the dealers need to recover fixed transaction
costs through the bid-ask spread. The fixed cost will be lower if the dealers make a large volume
of trades. Therefore, the model will imply the negative relationship between trading volume and
spread.
Using a sample of 182 matched restatement announcements from companies trading in
NYSE and AMEX, we first examine the time series of changes in spread and trading volume,
and return around the announcement date. We do not document any substantial change in trading
volume as well as in spread prior to the announcement. However, we do find substantial change
in volume and spread after the announcement. Specifically, spread increases dramatically at day
0 and day 1 (relative to the announcement day), and persistently go back the normal level after
that. Significant increases in trading volume begin at the announcement date and go back the
normal level after about 10 trading days. This is consistent with the models of Kyle (1985),
Easley and O’Hara (1982) and Kim and Verrecchia (1991a, 1991b) in that increased information
asymmetry at announcement dates should result in higher trading volumes as well as increased
spreads. We speculate that the directors or corporate insiders do have private information, but
they are not allowed to trade before the announcement made public. Therefore, right after the
announcement, the market makers will increase bid-ask spread because they believe that
corporate insiders will make use of their information plus their superior-information processing
abilities.
We also postulate a cross sectional model in which spread is a function of normal trading
volume, unusual trading volume, and return variability. As predicted by the inventory control
5
model, we find that spread is negatively correlated to trading volume but positively correlated to
return variability. We also find that unusual trading volumes which proxies for asymmetric
information are significantly positively related to spread, as predicted by the adverse selection
model.
The paper now proceeds as follows. In section II presents the data. Section III discusses
the methodologies used in this study. Section IV presents the summary empirical results.
II. Data
Our restatement announcement sample is obtained from GAO Financial Statement
Restatement Database released on January 2003 by the United States General Accounting
Offices for the period 1997-2002. We include only those companies whose stocks are trading in
NYSE and AMEX. Then, we select only companies which have data on book value, SIC code,
return, and intraday transaction data available during the period 1997-2002. We obtain book
value, SIC code from Compustat tape, stock return from CRSP database, spread from NYSE
TAQ. Table 1 presents some statistics on the sample.
We use four measures of spread. Dollar spread is the difference between the ask and bid
prices. Proportion dollar spread is the dollar spread divided by the bid-ask mid point. Effective
spread is two times the absolute value of the difference between transaction price and bid-ask
midpoint. Proportional effective spread is effective spread scaled by transaction price. We use
Lee and Ready (1991) methodology using 5-second delay to match quotes and transaction prices.
Daily spread is the average of spread for every quote reported during the days.
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III. Methodology
A. Univariate test
We examine abnormal volume, abnormal return, and abnormal spread around the
restatement announcements and compare with those of the matching firms.
Matching firms
First we find matching firms for the restatement companies based on SIC code, book to
market value, and size and then analyze effect of the announcements on trading activity based on
the differences between the restatement firms and the matching firms. This procedure allows us
to control for confounding market- and industry-wide effects and size and book-to-market factors
that might affect return and volume.
We define the universe of possible matching firms as all firms in the intersection of
CRSP and Compustat, with financial statement data available as of the most recent month-end at
least 30 days before the announcement date. From this, we then select all firms that have the
same two-digit SIC code as the restatement company, with size between 70% to 130%. Out of
these possible firms, we then select matching firm that has the closest book-to-market ratio. If we
are unable to find the match using the above criteria, we relax our size constraint to +/- 80%, and
next relax our industry constraint to only a one-digit SIC code match. Finally, we remove the
industry constraint completely to locate matches for two remaining variables.
Measuring abnormal return, abnormal volume, abnormal spread
Abnormal return: We define abnormal return as the daily difference in returns between
target and its matching firm. Since our matching firm is based on industry, size, book to market,
the abnormal return should control for famous effects found in literature (i.e., industry, size and
book to market factors).
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Abnormal volume: We use the following method to determine abnormal volume. First,
define normal trading volume for a restatement company by taking average daily volume over an
estimation period, from day –270 to –60 and from day +60 to +270 relative to the announcement.
After that, we compute a time series of abnormal volume by taking the daily volume on a given
day less normal volume calculated above, and divided by normal volume. This abnormal volume
considers firm-specific trading only. It does not take into account of other effect that may affect
volume but unrelated to the announcement. Therefore, we repeat the above steps for the
matching firms and compare with those restatement firms.
To compute abnormal spread, we use the same procedure as in calculating abnormal
volume.
B. Cross sectional tests
We use cross sectional regression analysis to further examine the impact of trading
activities on spread during both nonevent and event trading. We use dummy variables to test for
event-related shifts in the intercept and the slope coefficients on the variables studied. The event
window is split into three subperiods: PRE (day –15 through day –2), DURING (day –1 and day
0), and POST (day +1 through day +15). All days are relative to the announcement date (day 0).
We use trading volume, excess volume, and return variability to proxy for sources of bid-ask
spread documented in literature, i.e., order processing costs (trading volume), adverse selection
costs (excess volume) and inventory cost (trading volume and return variability). The model is as
follows:
0 1 2 3 4 5 6it it it itSPREAD a a retsq a vol a xvol a PRE a DURING a POST= + + + + + +
+ 7 8 9it it ita PRE vol a DURING vol a POST vol× + × + ×
8
+ 10 11 12it it it ita PRE xvol a DURING xvol a POST xvol ε× + × + × +
where is the squared return, is trading volume, and itretsq itvol itxvol is excess volume defined as
the difference between actual daily trading volume and its average trading volume over time.
PRE, DURING, POST are dummy variables defined as above.
itPRE vol× , , itDURING vol× itPOST vol× measure impact of trading volume on spread during
the event window covering –15 to +15 days.
itPRE xvol× , , itDURING xvol× itPOST xvol× : measure impact of excess trading volume during
the event window covering –15 to +15 days.
IV. Results
A. Results from univariate tests
Table 2 shows average daily abnormal returns for restatement firms and average daily abnormal
volume for restatement and matching firms. Before the announcement date, abnormal return
behaves quite steadily, they are small and statistically insignificant . However, on the
announcement date, abnormal return dramatically and significant negative AR of –2.38% are
produced. The decrease continues in day 1 with –3.08%, and reverse back to the normal level on
day 2 before dropping substantially again in day 3 with –1.73%. Clearly, the wealth effect is
negative and significant, and for the two and four day windows appear to be around -5.5% and –
7% respectively. The results are depicted graphically in Figure 2.
Consider now the results for abnormal volume presented in Table 2 and graphically produced in
Figure 1. The impact of restatement on trading activity is also dramatic. There is a sharp and
significant increase in abnormal volume of restating firms commencing on the day of the
9
announcement and persisting for 7 days. No significant change in abnormal volume is observed
for matching firms, and the difference between the restating and matching firms are also
significant.
Now consider the impact on bid-ask spread. Table 3 shows the abnormal dollar spread for
restating firm is insignificant and small before the announcement date, but increases substantially
and significantly on day 0 and day 1. It then drops back to the normal level. The abnormal spread
for the matched firms remain small and insignificant. The results are the same for all 4 measures
of spreads (for space consideration, only dollar spread results are reported). Figure 3 shows the
impact on the abnormal dollar spread and Figure 4 illustrates dramatically the effect on abnormal
effective spread. These results are consistent with Kim and Verrecchia (1994) in that corporate
insiders may have private information before the announcements, but they are prohibited from
trading. Therefore, once the announcements have been made public, they are able to process
information much faster and better than other investors. Hence, market makers will increase the
spread to compensate for this adverse selection costs.
B. Results from the cross sectional tests Table 4 presents the results from the cross sectional tests. Four alternative measures of spread are
used as the dependent variable. White’s correction for hetroskasticity are used in the regressions.
Here are the salient points. First, spread is negatively and significantly related to trading volume
and positively related to return variability. This appears to be consistent with inventory control
model. Second, spread is positively related to excess trading volume (proxy for informed
trading). This supports the adverse selection model. The results are similar for all four measures
of spread.
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Dummy variables are used to find out whether there is any changes in the spread in the event
period which is not accounted for by normal and excess trading volume or by return variability.
Significant coefficients on the dummies would suggest that the bid-ask spread during the event
window reflects changes in information asymmetry or inventory costs which are not entirely
capture by the explanatory variables. The dummy variables cover the period immediately before,
during, and immediately after the event. The PRE dummy covers –15 to –2 days, DURING
covers days 0 and 1 and POST covers +2 to +15 days.
Consider now the dummy variables for vol and xvol.
• , , itPRE vol× itDURING vol× itPOST vol× measure impact of trading volume on spread
during the event window which covers days –15 through +15. These dummy variable
coefficients are significant for the itPOST vol× measure.
• , , itPRE xvol× itDURING xvol× itPOST xvol× : measure impact of excess trading volume
during the event window which covers days –15 through +15. The dummy
variable is significant. This suggests the relationship between xvol and spread declined in the
pre-period just before the event.
itPRE xvol×
In the cross sectional model the spread is postulated to be a function of normal trading
volume, unusual trading volume, and return variability. As predicted by the inventory control
model, we find that spread is negatively correlated to trading volume but positively correlated to
return variability. We also find that unusual trading volumes which proxies for asymmetric
information are significantly positively related to spread, as predicted by the adverse selection
model.
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Table 1: Descriptive Statistics: Restatement and Matching Firms
The table provides descriptive statistics for our sample from 1997-2002. Variables are estimated the end of the most recent month-end at least 30 days before the restatement announcement date. Restatement firms and their matches must have CRSP share price, volume, number of shares outstanding data, positive common equity and SIC code provided by Compustat. Restatement and matching firms must trade on NYSE or AMEX. Number of shares outstanding is in million shares, market capitalization is in million dollars. Betas are estimated using the past 2 year window. Our methodology to find the matching firms is described in detail in Section III. Briefly, we select all firms that have the same two-digit SIC code as the restatement company, with size between 70% to 130%. Out of these possible firms, we then select matching firm that has the closest book-to-market ratio. If we are unable to find the match using the above criteria, we relax our size constraint to +/- 80%, and next relax our industry constraint to only a one-digit SIC code match. Finally, we remove the industry constraint completely to locate matches for two remaining variables Panel A provides descriptive statistics of restatement and matching firms in terms of share prices, number of shares outstanding,, market capitalization, book-to-market, and beta. Panel B gives a breakdown of the sample into reasons of restatement announcements. There are 9 reasons: (1) acquisitions and mergers, (2) cost or expenses, (3) in-process research and development, (4) reclassification, (5) related-party transactions, (6) restructuring, assets or inventory, (7) revenue recognition, (8) security related, and (9) other reasons (see GAO database for more detail). Panel C divide the sample according to the prompter of the restatement announcements: (1) SEC, (2) auditors, (3) company, and (4) other entities.
Panel A: Sample descriptive statistics of restatement and matching firms Number of
Restatements Median
Restatement Median Match
Mean Restatement
Mean Match
Share Prices ($) 182 21.97 25.34 27.14 30.53 Shares Outstanding (millions) 182 52.66 52.20 193.56 225.70 Market Capitalization ($millions) 182 1095.29 1408.59 6413.90 9454.09 Book-to-market 182 0.52 0.48 0.76 0.60 Beta 182 0.62 0.61 0.75 0.74
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Panel B: Restatements by Reasons
Year Total (1) (2) (3) (4) (5) (6) (7) (8) (9) 1997 9 1 3 0 1 0 1 1 1 1 1998 9 1 3 0 0 0 0 1 0 4 1999 31 4 1 4 0 0 12 7 0 3 2000 36 2 4 0 2 1 7 14 1 5 2001 55 0 4 0 1 4 6 31 2 7 2002 42 2 3 0 1 7 8 8 8 5 Total 182 10 18 4 5 12 34 62 12 25
Panel C: Restatements by Prompters
Year Total SEC Auditor Company Other 1997 9 1 0 4 4 1998 9 2 0 6 1 1999 31 15 2 6 8 2000 36 4 3 14 15 2001 55 4 3 15 33 2002 42 6 5 18 13 Total 182 32 13 63 74
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t-score Restatement t-scores Match t-scores Restatement t-scores Abnormal Abnormal Abnormal restatement Abnormal match Less restatement
Days Return Return Volume Volume Match less match-15 1.06% 2.20 1.25% 0.09 15.68% 1.24 -14.43% -0.78 -14 0.07% 0.19 -12.74% -1.45 9.09% 1.06 -21.83% -1.82 -13 -0.61% -1.51 -4.59% -0.63 1.36% 0.14 -5.95% -0.49 -12 0.31% 0.68 -13.88% -2.21 -11.83% -1.17 -2.05% -0.18 -11 0.08% 0.15 -12.59% -2.09 -1.71% -0.21 -10.88% -1.09 -10 -0.61% -0.94 -22.81% -3.50 -5.30% -0.38 -17.51% -1.15 -9 -0.37% -1.03 -4.87% -0.51 -0.49% -0.04 -4.38% -0.31 -8 0.99% 2.36 -9.98% -0.89 -13.29% -1.69 3.31% 0.25 -7 0.17% 0.40 -14.37% -2.31 -2.21% -0.23 -12.16% -1.11 -6 0.13% 0.38 -10.96% -1.71 9.55% 0.59 -20.51% -1.22 -5 -0.21% -0.53 -16.12% -3.04 7.83% 0.57 -23.95% -1.67 -4 0.00% 0.00 13.29% 0.97 -4.77% -0.63 18.06% 1.19 -3 0.23% 0.36 3.90% 0.28 8.36% 0.60 -4.46% -0.22 -2 -0.47% -1.08 4.05% 0.39 -0.35% -0.03 4.40% 0.30 -1 0.42% 1.32 2.80% 0.29 -5.01% -0.62 7.81% 0.69 0 -2.38% -3.32 145.65% 3.98 17.69% 1.57 127.96% 3.47 1 -3.08% -3.29 272.38% 4.66 0.06% 0.01 272.32% 4.64 2 0.23% 0.46 142.58% 3.04 -10.93% -1.13 153.51% 3.19 3 -1.73% -3.85 74.54% 2.83 9.23% 0.43 65.30% 2.01 4 -0.53% -0.82 48.21% 2.53 -9.22% -1.11 57.42% 2.72 5 -0.63% -1.12 48.16% 2.80 -7.12% -1.14 55.28% 3.00 6 0.29% 0.83 24.30% 2.14 -6.41% -0.83 30.71% 2.27 7 0.04% 0.09 18.82% 2.25 -6.45% -0.92 25.27% 2.23 8 0.36% 0.54 11.13% 1.24 -13.48% -2.16 24.60% 2.28 9 0.11% 0.26 14.83% 1.43 -4.98% -0.57 19.81% 1.56 10 -0.22% -0.45 12.01% 1.08 1.28% 0.10 10.72% 0.63 -0.22% -0.45 12.01% 1.08 1.28% 0.10 10.72% 0.63
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Table 2: Daily Return and Volume The table shows average daily abnormal returns for restatement firms and average daily abnormal volume for restatement and matching firms. Abnormal returns are calculated by subtracting raw matching firm returns from raw restatement firm return. Abnormal volume is measured as daily volume minus normal volume scaled by normal volume. Normal volume is estimated from two periods (-270 to –60) and (30 to 270).
Table 3: Daily Average Abnormal Spread
The table shows average abnormal dollar spread for restatement and matching firms. Dollar spread is the difference between ask and bid price. Normal dollar spreads are estimated from days –270 through –60 and from days +30 through +270. Abnormal spread is calculated by subtracting normal spread from daily spread, and dividing the standard deviation of spread over the estimation period.
Restatement t-scores Match t-scores Restatement t-scores Abnormal restatement Abnormal match Less restatement
Day Dollar Spread Dollar Spread Match less match -15 -0.03 -0.35 -0.10 -1.14 0.07 0.62 -14 -0.04 -0.63 -0.13 -1.72 0.09 0.86 -13 0.10 1.08 0.20 0.80 -0.10 -0.39 -12 -0.01 -0.09 -0.02 -0.18 0.01 0.09 -11 -0.14 -1.66 -0.02 -0.22 -0.12 -0.92 -10 -0.19 -2.07 -0.18 -1.69 -0.01 -0.07 -9 -0.04 -0.49 -0.11 -1.07 0.07 0.49 -8 0.00 -0.03 -0.07 -0.82 0.07 0.56 -7 -0.07 -0.92 -0.03 -0.42 -0.04 -0.40 -6 0.07 0.68 -0.09 -1.10 0.16 1.23 -5 -0.05 -0.68 0.11 1.16 -0.16 -1.33 -4 0.01 0.11 -0.11 -1.29 0.12 1.00 -3 -0.17 -1.94 -0.17 -1.62 0.00 0.02 -2 0.08 0.73 -0.12 -1.28 0.21 1.39 -1 -0.02 -0.34 -0.16 -1.84 0.14 1.22 0 0.28 2.90 -0.04 -0.48 0.32 2.54 1 0.40 1.63 -0.11 -1.32 0.51 1.97 2 -0.06 -0.63 0.02 0.21 -0.08 -0.61 3 -0.06 -0.56 -0.02 -0.17 -0.04 -0.26 4 -0.12 -1.27 -0.15 -1.77 0.03 0.26 5 -0.05 -0.44 -0.01 -0.08 -0.03 -0.17 6 -0.15 -2.09 -0.28 -3.03 0.13 1.09 7 -0.18 -2.58 -0.02 -0.19 -0.16 -1.50 8 -0.16 -2.32 0.13 1.06 -0.29 -2.06 9 -0.10 -1.24 -0.04 -0.38 -0.06 -0.49
10 -0.20 -2.69 -0.17 -1.84 -0.02 -0.18 11 -0.10 -0.98 -0.21 -1.89 0.11 0.73 12 -0.22 -2.06 -0.26 -2.87 0.04 0.30 13 -0.11 -1.57 -0.12 -1.17 0.01 0.06 14 -0.21 -3.24 0.00 -0.06 -0.21 -2.01 15 -0.25 -3.55 -0.07 -0.98 -0.18 -1.74
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Table 4: OLS Estimation of the Cross-Sectional Determinants of Restatement Firm Spread
during Nonevent Trading and around the restatement announcements
0 1 2 3 4 5 6it it it itSPREAD a a retsq a vol a xvol a PRE a DURING a POST= + + + + + + + 7 8 9it it ita PRE vol a DURING vol a POST vol× + × + × + 10 11 12it it it ita PRE xvol a DURING xvol a POST xvol ε× + × + × +
The table presents the regression results of spread on other variables as in the above model. Four measures of spread are used. They are dollar spread, proportional spread (dollar spread divided by the bid-ask midpoint), effective spread (two times the absolute value of the difference between the bid-ask midpoint and the transaction price), and proportional effective spread (effective spread divided by the transaction price). Definition of variables: retsq is return squared; vol is volume; xvol is excess trading volume (defined as the difference between actual daily trading volume and its average trading volume over time; PRE is a dummy variable (= 1 for days –15 through –2 relative to the announcement date, PRE = 0 all other days); DURING is a dummy variable (= 1 for days –1 through 0 relative to the announcement date, DURING = 0 all other days); POST is a dummy variable (=1 for days +1 through +15 relative to the announcement date, POST = 0 all other days). The regression coefficients and their p-values (adjusted for heteroskedasticity) are reported
Panel A: Dollar Spread Panel B: Effective Spread Variable Coefficient p-value Coefficient p-value Constant 0.1315 0.000 0.0972 0.000 retsq 0.3955 0.001 0.4087 0.001 vol -0.0054 0.000 -0.0010 0.0003 xvol 0.0056 0.000 0.0020 0.000 PRE -0.0083 0.153 -0.0064 0.274 DURING 0.0015 0.913 0.0089 0.516 POST -0.0192 0.000 -0.0137 0.0005 PRE vol× 0.0006 0.483 0.0018 0.1905 DURING vol× 0.0001 0.937 -0.0008 0.6602 POST vol× 0.0016 0.001 0.0017 0.0902 PRE xvol× -0.0025 0.000 -0.0023 0.0446 DURING xvol× -0.0008 0.606 -0.0014 0.5657 POST xvol× -0.0006 0.505 0.0025 0.1451
18
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Panel C: Proportional Dollar Spread Panel B: Proportional Effective
Spread Variable Coefficient p-value Coefficient p-value Constant 0.0102 0.000 0.0072 0.000 retsq 0.3198 0.000 0.2605 0.000 vol -0.0007 0.000 -0.00006 0.0502 xvol 0.0006 0.000 0.00012 0.0064 PRE -0.0010 0.0274 -0.00073 0.0786 DURING -0.0017 0.0753 -0.00092 0.2354 POST 0.0010 0.1641 0.00112 0.0617 PRE vol× -0.00004 0.6649 0.00008 0.6914 DURING vol× 0.0002 0.3714 -0.00018 0.2002 POST vol× -0.0001 0.1356 -0.00021 0.1436 PRE xvol× -0.0005 0.000 -0.00039 0.0279 DURING xvol× -0.0007 0.0289 -0.00042 0.1192 POST xvol× -0.0004 0.0034 -0.00012 0.4858
Figure 1: Abnormal Volume around Restatement Announcements
This figure presents the abnormal trading volume around the restatement announcements for restatement firms and matching firms from day -60 to day 30. Abnormal volume is measured as daily volume minus normal volume scaled by normal volume. Normal volume is estimated from two periods (-270 to –60) and (30 to 270).
-0.5
0
0.5
1
1.5
2
2.5
3
-60 -50 -40 -30 -20 -10 0 10 20 30
restatement firm match firm
20
Figure 2: Abnormal Return around Restatement Announcements
This figure presents the abnormal return around the restatement announcements from day -60 to day 30. Abnormal returns are calculated by subtracting raw matching firm returns from raw restatement firm return.
-3.50%
-3.00%
-2.50%
-2.00%
-1.50%
-1.00%
-0.50%
0.00%
0.50%
1.00%
1.50%
-60 -50 -40 -30 -20 -10 0 10 20 30
21
Figure 3: Abnormal Dollar Spread around Restatement Announcements.
This figure presents the mean abnormal dollar spread for the restatement and matching firms before and after the restatement announcements, from day –60 to day +30, where the announcement occurs on day 0. Dollar spread is the average difference between the ask and bid price for all transactions for a given firm on that day. Normal spread is estimated for each firm using average daily spread from day –270 to –60 and from +30 to +270. Abnormal spread is calculated as dollar spread less normal spread divided by the standard deviation of normal spread.
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
-60 -50 -40 -30 -20 -10 0 10 20 30
match restatement
22
Figure 4: Abnormal Effective Spread around Restatement Announcements.
This figure presents the mean abnormal effective spread for the restatement and matching firms before and after the restatement announcements, from day –60 to day +30, where the announcement occurs on day 0. Effective spread is the average of two times the absolute difference between the midpoint and the transaction price for all transactions for a given firm on that day. Normal effective spread is estimated for each firm using average daily spread from day –270 to –60 and from +30 to +270. Abnormal effective spread is calculated as effective spread less normal effective spread divided by the standard deviation of normal effective spread.
-0.5
0
0.5
1
1.5
2
-60 -50 -40 -30 -20 -10 0 10 20 30
match restatement
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